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Equality-minded treatment choice

Citations

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Cited by:

  1. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  2. Shota FUJISHIMA & Tadao HOSHINO & Shinya SUGAWARA, 2020. "Heterogeneous Treatment Effects of Place-based Policies: Which Cities Should be Targeted?," Discussion papers 20036, Research Institute of Economy, Trade and Industry (RIETI).
  3. Yuehao Bai & Azeem M. Shaikh & Max Tabord-Meehan, 2024. "A Primer on the Analysis of Randomized Experiments and a Survey of some Recent Advances," Papers 2405.03910, arXiv.org, revised Apr 2025.
  4. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.
  5. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
  6. Anders Bredahl Kock & David Preinerstorfer, 2024. "Regularizing Fairness in Optimal Policy Learning with Distributional Targets," Papers 2401.17909, arXiv.org, revised May 2025.
  7. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  8. Emily Breza & Arun G. Chandrasekhar & Davide Viviano, 2025. "Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery," Papers 2501.13355, arXiv.org, revised Jul 2025.
  9. Hugo Bodory & Federica Mascolo & Michael Lechner, 2024. "Enabling Decision-Making with the Modified Causal Forest: Policy Trees for Treatment Assignment," Papers 2406.02241, arXiv.org.
  10. Firpo, Sergio & Galvao, Antonio F. & Kobus, Martyna & Parker, Thomas & Rosa-Dias, Pedro, 2025. "Loss aversion and the welfare ranking of policy interventions," Journal of Econometrics, Elsevier, vol. 252(PB).
  11. Daido Kido, 2022. "Distributionally Robust Policy Learning with Wasserstein Distance," Papers 2205.04637, arXiv.org, revised Aug 2022.
  12. Yanqin Fan & Yuan Qi & Gaoqian Xu, 2025. "Policy Learning with $\alpha$-Expected Welfare," Papers 2505.00256, arXiv.org.
  13. Toru Kitagawa & Weining Wang & Mengshan Xu, 2022. "Policy Choice in Time Series by Empirical Welfare Maximization," Papers 2205.03970, arXiv.org, revised Nov 2025.
  14. Yue Fang & Geert Ridder & Haitian Xie, 2025. "Semiparametric Efficiency in Policy Learning with General Treatments," Papers 2512.19230, arXiv.org, revised Feb 2026.
  15. Anders Bredahl Kock & David Preinerstorfer & Bezirgen Veliyev, 2022. "Functional Sequential Treatment Allocation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1311-1323, September.
  16. Hirano, Keisuke & Porter, Jack R., 2020. "Asymptotic analysis of statistical decision rules in econometrics," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 283-354, Elsevier.
  17. Toru Kitagawa & Weining Wang & Mengshan Xu, 2024. "Policy choice in time series by empirical welfare maximization," CeMMAP working papers 27/24, Institute for Fiscal Studies.
  18. Giacomo Opocher, 2026. "Better Measurement or Larger Samples? Data Collection for Policy Learning with Unobserved Heterogeneity," Papers 2604.07181, arXiv.org.
  19. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org, revised Oct 2025.
  20. Ryo Okui, 2024. "The 2023 Japanese Economic Association Nakahara Prize: Recipient—Prof. Toru Kitagawa, Brown University and University College London," The Japanese Economic Review, Springer, vol. 75(3), pages 405-406, July.
  21. Kock, Anders Bredahl & Preinerstorfer, David & Veliyev, Bezirgen, 2023. "Treatment recommendation with distributional targets," Journal of Econometrics, Elsevier, vol. 234(2), pages 624-646.
  22. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
  23. Dalia Ghanem & D'esir'e K'edagni & Ismael Mourifi'e, 2023. "Evaluating the Impact of Regulatory Policies on Social Welfare in Difference-in-Difference Settings," Papers 2306.04494, arXiv.org, revised Nov 2025.
  24. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-Driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Dec 2025.
  25. Timothy Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," IFS Working Papers WCWP28/24, Institute for Fiscal Studies.
  26. Yue Fang & Junyi Liu & Jong-Shi Pang, 2025. "Treatment learning with Gini constraints by Heaviside composite optimization and a progressive method," Computational Optimization and Applications, Springer, vol. 92(2), pages 471-513, November.
  27. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2022. "Treatment Choice with Nonlinear Regret," Papers 2205.08586, arXiv.org, revised Oct 2024.
  28. Toru Kitagawa & Jeff Rowley, 2024. "Bandit algorithms for policy learning: methods, implementation, and welfare-performance," The Japanese Economic Review, Springer, vol. 75(3), pages 407-447, July.
  29. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2023. "Treatment choice, mean square regret and partial identification," The Japanese Economic Review, Springer, vol. 74(4), pages 573-602, October.
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